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网络上的数据源/文件位置是：https : //www.newyorkfed.org/medialibrary/media/survey/empire/data/esms_seasonallyadjusted_diffusion.csv 但是由于存在连接问题，我保存了它（'esms_seasonallyadjusted_diffusion.csv'）无论如何，在本地这是最好的速度，我也将它保存到 github： https://github.com/me50/hlar65/blob/master/ESMS_SeasonallyAdjusted_Diffusion.csv'）

2个问题：

  1. 尝试访问网络链接时（即使单击它会下载文件）我收到连接错误：“URLError: <urlopen error Tunnel connection failed: 403 Forbidden>”
  2. 我的代码（我是初学者！）看起来很笨拙。有没有更干净更好的表达方式？

谢谢大家的帮助'''

import pandas as pd
import numpy as np
from pandas.plotting import scatter_matrix
import scipy as sp
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sn


df = dd.read_csv('https://www.newyorkfed.org/medialibrary/media/survey/empire/data/esms_seasonallyadjusted_diffusion.csv')
 
df = df.rename(columns={'surveyDate':'Date',
                        'GACDISA': 'IndexAll', 
                        'NECDISA': 'NumberofEmployees',
                        'NOCDISA': 'NewOrders',
                        'PPCDISA': 'PricesPaid',
                        'PRCDISA': 'PricesReceived'})
headers = df.columns

df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)

IndexAll = df['IndexAll']
NumberofEmployees = df['NumberofEmployees']
NewOrders = df['NewOrders']
PricesReceived = df['PricesReceived']

data = df[['IndexAll', 'NumberofEmployees', 'NewOrders', 'PricesReceived']]
data2 = data.copy()

ds = data2
FS_A = 14
FS_L = 16
FS_T = 20
FS_MT = 25

fig, ((ax0, ax1), (ax2,ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(20,15))

# density=True : probability density i.e. prb of an outcome; False = actual # of frequency

ds['IndexAll'].plot(ax=ax0, color='red')
ax0.set_title('New York Empire Manufacturing Index', fontsize = FS_T)
ax0.set_ylabel('Date', fontsize = FS_A) 
ax0.set_xlabel('Empire Index', fontsize = FS_L) 
ax0.tick_params(labelsize=FS_A)

ds['NumberofEmployees'].plot(ax=ax1, color='blue')
ax1.set_title('Empire: Number of Employees', fontsize = FS_T)
ax1.set_ylabel('Date', fontsize = FS_L) 
ax1.set_xlabel('Number of Employees', fontsize = FS_L) 
ax1.tick_params(labelsize=FS_A)

ds['NewOrders'].plot(ax=ax2, color='green')
ax2.set_title('Empire: New Orders', fontsize = FS_T)
ax2.set_ylabel('Date', fontsize = FS_L) 
ax2.set_xlabel('New Orders', fontsize = FS_L) 
ax2.tick_params(labelsize=FS_A)

ds['PricesReceived'].plot(ax=ax3, color='black')
ax3.set_title('Empire: Prices Received', fontsize = FS_T)
ax3.set_ylabel('Date', fontsize = FS_L) 
ax3.set_xlabel('Prices Received', fontsize = FS_L) 
ax3.tick_params(labelsize=FS_A)

fig.tight_layout()
fig.suptitle('New York Manufacturing Index Main Components - Showing the Depths of COVD19 in 2020', fontsize = FS_MT)
fig.tight_layout()
fig.subplots_adjust(top=0.88)
fig.subplots_adjust(bottom = -0.2) 
fig.savefig("Empire.png")
plt.show()

'''

4

1 回答 1

1

对于第一个问题，您是否设置了任何代理？我认为它来自代理设置。

关于第二个我可以做一些清理，但它非常依赖于开发人员的代码风格。您可以用几种不同的方式编写脚本。

注意：

  • 您可以在 read_csv 调用中解析日期
  • 使用括号一步完成所有你想用 df 做的事情
  • 您可以为参数定义数组并在 for 循环中绘制所有图
df = (
    pd
    .read_csv('https://www.newyorkfed.org/medialibrary/media/survey/empire/data/esms_seasonallyadjusted_diffusion.csv',
             parse_dates=['surveyDate'])
    .rename(columns={'surveyDate':'Date',
                        'GACDISA': 'IndexAll', 
                        'NECDISA': 'NumberofEmployees',
                        'NOCDISA': 'NewOrders',
                        'PPCDISA': 'PricesPaid',
                        'PRCDISA': 'PricesReceived'})
    .set_index('Date')
)

FS_A = 14
FS_L = 16
FS_T = 20
FS_MT = 25

titles = ['New York Empire Manufacturing Index','Empire: Number of Employees','Empire: New Orders','Empire: Prices Received']
xlabels = ['Empire Index','Number of Employees','New Orders','Prices Received']
colors=['red','blue','green','black']
columns = ['IndexAll', 'NumberofEmployees', 'NewOrders', 'PricesReceived']
ds = df[columns]
k=0
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20,15))
for i in range(2):
    for j in range(2):
        ds[columns[k]].plot(ax=axes[i][j], color=colors[k])
        axes[i][j].set_title(titles[k], fontsize = FS_T)
        axes[i][j].set_ylabel('Date', fontsize = FS_A) 
        axes[i][j].set_xlabel(xlabels[k], fontsize = FS_L) 
        axes[i][j].tick_params(labelsize=FS_A)
        k+=1
于 2020-10-11T23:52:39.367 回答